PulmCrit- Ranking antibiotics in order of allergenicity

Introduction with a case

An elderly man was admitted in transfer with methicillin-sensitive staph aureus (MSSA) endocarditis complicated by cerebral infarctions. He had a history of experiencing facial swelling due to penicillin some decades earlier. Due to fear of anaphylaxis from penicillin, he was treated with ceftriaxone (for coverage of MSSA and brain penetration). Unfortunately he continued to deteriorate.

Upon transfer his antibiotic regimen was re-considered. The use of ceftriaxone for MSSA bacteremia is controversial, with some evidence suggesting that ceftriaxone treatment correlates with increased mortality (Lother 2017). Nafcillin would be the ideal antibiotic for his MSSA endocarditis with brain involvement. However, there was fear of possible cross-reaction with penicillin. How dangerous would it be to treat him with nafcillin?

Literature review revealed only one case of nafcillin-induced anaphylaxis or angioedema, of questionable validity (anaphylaxis occurred after the second dose rather than the first; Silverman 1984). His history of penicillin reaction was remote and vague. To test whether he was actually allergic, he was given one dose of nafcillin very slowly under controlled conditions (he happened to be intubated at that point in time). He tolerated that fine and was transitioned to full-dose nafcillin therapy.

Predicting allergy: patient-based vs. population-based approaches

Our current approach to allergy is primarily patient-based. This focuses on the patient’s prior history of reaction: how severe was it, when was it, how certain are we that it was truly allergic? This strategy has been proven to be inaccurate. For example, ~90% of patients who report a penicillin allergy are not allergic when skin-tested.

An alternative strategy is a population-based approach. For example, there don’t appear to be any reports of anaphylaxis to meropenem in the literature. Studies have reported meropenem administration to hundreds of patients without severe allergic reaction (including some with penicillin allergy). Thus, regardless of the patient’s allergy history, it is unlikely that they will have a severe allergic reaction to meropenem.

A population-based strategy has some potential advantages in critical care, where we often don’t have detailed information about our patients. For example, if a crashing septic patient has allergies to every known antibiotic, they could still be treated with meropenem with a low risk of severe reaction. This decision could be made efficiently, without interviewing their family about the details of every one of their allergic reactions.

Ideally, our decision-making should incorporate both patient-based and population-based reasoning. Combining patient-specific factors as well as epidemiologic factors isn’t anything novel – it’s integral to most clinical reasoning. When considering what diagnosis a patient has, we are continuously synthesizing specifics about the patient with the epidemiology of various potential diagnoses.

Theory behind a population-based approach

For a population-based approach to be helpful, the risk of severe allergic reactions must differ substantially between different antibiotics. This will be tested further below, but we could probably agree that it has face validity. Some antibiotics (e.g. penicillin) seem to cause lots of trouble. Other antibiotics (e.g. doxycycline, meropenem) simply don’t.

You may be nodding – experienced practitioners are well aware of these differences, and we already take this into account. The issue is whether we could be more precise and evidence-based about how we do this. To move from our own intuition to a more evidence-based approach, we need accurate information about the relative risk of anaphylaxis from different antibiotics. Unfortunately, such information seems to be lacking. I’ve been unable to find a good reference comparing the relative allergenicity of various antibiotics (1).

My wacky attempt at ordering antibiotics in order of allergenicity

Below is an effort to rank-order antibiotics which I made during a few spare hours in the hospital overnight. I don’t think this list is very reliable, nor that it should be used to make clinical decisions. The goal here is simply to illustrate that creating such a list isn’t necessarily that difficult.

I started by creating two metrics to look at various antibiotics:

Anaphylaxis Hits: Number of articles retrieved from PubMed by searching for the antibiotic name plus either anaphylaxis or angioedema.

Anaphylaxis Index: Number of anaphylaxis hits normalized to the total number of articles about that antibiotic:

Obviously these are blunt tools. For example, an article stating that a certain antibiotic doesn’t cause anaphylaxis would be measured as an “anaphylaxis hit.” However, when leveraged across more than 27 million citations in PubMed, this could potentially find certain signals.

To test the validity of these measurements, I compared them to three large epidemiologic series of patients with anaphylaxis (Faria 2014, Lee 2017, Renaudin 2013). The results are shown below. Antibiotics are sorted in descending order based on the anaphylaxis index.

Although this is a crude approach, it seemed to perform surprisingly well. For example, ten antibiotics were identified as the safest. Among these, none caused any episodes of anaphylaxis among any of the three epidemiologic series (which contained a total of 212 reactions to the drugs listed above).

The algorithm neatly classified all antibiotics from the aminoglycoside and carbapenem classes into the top six safest antibiotics. This is consistent with general opinion and prior research on meropenem, supporting the validity of this algorithm.

Further work is obviously needed to validate this strategy. Someone with more resources and experience in bioinformatics could do a much better job – and hopefully will. Or maybe a programmer at Google with a supercomputer and a better algorithm.

Convergent information

Ideally patient-based and population-based information could be used synergistically to reach a common truth. One example of this from the above data is the case of cefepime.

For years, we were taught that patients with a penicillin allergy had a 10% rate of cross-reactivity with all cephalosporins. Furthermore, we were taught that many patients had a penicillin allergy with cross-allergy extending across all beta-lactam antibiotics. These concepts are inconsistent with the data shown below, which reveals a huge difference in anaphylaxis rates between cefazolin and cefepime. This population data implies that there is no such thing as a “cephalosporin allergy” or “beta-lactam allergy,” but rather that patients are allergic to specific drugs (e.g. cefazolin but not cefepime).

Over the last few years the concept of 10% cross-reactivity and pan-beta-lactam allergy have finally been debunked using patient-based data (careful studies examined penicillin-allergic patients to determine whether they were cross-allergic to various cephalosporins). It was found that allergy exists to side-chains unique to specific antibiotics, rather than the common beta-lactam core structure (debunking the existence of a pan-beta-lactam allergy). It is notable that this could have been easily predicted from the above population-based data set, which is a coarse amalgam of mostly old data. This illustrates the power of large population datasets to answer specific questions, without requiring much work.

The absolute risk of anaphylaxis varies dramatically between different antibiotics.

The majority of severe reactions may be due to a limited group of antibiotics.

Some antibiotics exist with little or no risk of a severe anaphylaxis.

More precise data regarding the absolute risk of reaction to different antibiotics could help us make more evidence-based decisions about antibiotic selection (especially in patients with vague allergy history).